Metrics are an informal measure of different aspects of a model or method. Currently, proposed metrics for explanatory methods such as Infidelity or Sensitivity [1]} focus on the evaluation through the importance of an attributed feature to the predicted output accuracy as a proxy measure for a faithful explanation. Relying on metrics which measure the same proxy behaviour to evaluate faithfulness can be dangerous since research in the area of metrics has already shown how volatile rankings of methods are depending on the selected metric for evaluation [2]}. It would be unreasonable to believe that this was also not the case for IML. Further, metrics evaluating faithfulness have to be adapted to also measure global explanations and must apply to different query classes. The usefulness of a model in statistics, but also in other sciences, is commonly evaluated by its complexity, primarily measured through employing information theory. The first work in this area for interpretable models was done by [3]}. Metrics measuring different aspects of a method can serve as the basis for hypothesis testing and also express the confidence of an explanation.
